{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.0\n"
     ]
    }
   ],
   "source": [
    "temp = 212.0\n",
    "\n",
    "def toCelsius(fahrenheit):\n",
    "    return (fahrenheit - 32) * 5.0 / 9.0\n",
    "\n",
    "print toCelsius(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imports run.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas.io import gbq\n",
    "\n",
    "print \"Imports run.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running query...\n",
      "Requesting query... ok.\n",
      "Query running...\n",
      "Query done.\n",
      "Cache hit.\n",
      "\n",
      "Retrieving results...\n",
      "Got 40 rows.\n",
      "\n",
      "Total time taken 1.2 s.\n",
      "Finished at 2017-03-24 19:46:14.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>avg_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1969</td>\n",
       "      <td>7.203189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970</td>\n",
       "      <td>7.222404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1971</td>\n",
       "      <td>7.245800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1972</td>\n",
       "      <td>7.254677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1973</td>\n",
       "      <td>7.266844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  avg_weight\n",
       "0  1969    7.203189\n",
       "1  1970    7.222404\n",
       "2  1971    7.245800\n",
       "3  1972    7.254677\n",
       "4  1973    7.266844"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projectId = \"cloud-training-demos\"  # CHANGEME\n",
    "sql = \"\"\"\n",
    "SELECT\n",
    "  year,\n",
    "  AVG(weight_pounds) AS avg_weight\n",
    "FROM\n",
    "  publicdata.samples.natality\n",
    "GROUP BY\n",
    "  year\n",
    "ORDER BY\n",
    "  year ASC\n",
    "\"\"\"\n",
    "\n",
    "print 'Running query...'\n",
    "data = gbq.read_gbq(sql, project_id=projectId)\n",
    "\n",
    "data[:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KTjWQnRrpl5deLuXaMWkE2G28tOIQ/1h1mDanmxvHK6CJ+AsFEJFLWLujhH++cwTDgG/e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\ndRQQAABg3f8CGuE/vb6QFkAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63a4d87890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='year', y='avg_weight');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
